Current systems can allow for visualizing three-dimensional (“3D”) objects obtained with, for example, imaging devices, other systems and/or inputs. Currently, 3D objects can be processed for 3D printing, visualized on a two-dimensional (“2D”) screen, and/or visualized in augmented and/or virtual reality. Typically the 3D object is manually processed through various systems such that it can be 3D printed, visualized on a 2D screen, and/or visualized in augmented and/or virtual reality. For example, in order to 3D print a 3D object, the 3D object can be segmented, masks created, and/or a mesh created.
Typically, manual transformation can require that a user engage multiple systems, provide inputs/outputs for each system, and/or understand how to run each system. This can be time consuming and unrealistic for a medical professional to perform. Manual processing of 3D objects (e.g., transforming 3D objects and moving the data between systems) can contribute to introduction of errors.
Therefore, it can be desirable to provide an end-to-end system that can allow 3D objects to be rendered for a 2D screen, rendered for virtual reality and/or 3D printed. It can also be desirable to provide a system for volume rendering that has sufficient speed such that a user can zoom into and out of the visualized volume, modify the visualized volume, create one or more masks from the 3D object and/or create one or more mesh from the 3D object.
The one or more masks can be based on a type of the object of the 3D object. For example, for a CT scan of a heart, the corresponding masks can include a right ventricle mask, a left ventricle mask, a right atrium mask, a left atrium mask, an aorta mask, a pulmonary artery mask, a blood volume mask and/or a soft tissue mask. A portion of the CT scan data can correspond to each mask and can be assigned to its respective mask accordingly.
The masks can be used to visualize the 3D object on a two-dimensional (“2D”) screen. In some scenarios, the 3D object can be missing data or can include extraneous data. In both cases, a mask created from that imaging data, when rendered into a format that is suitable for viewing on a 2D screen, can appear erroneous to the viewer. In some systems, the masks can be used as a basis for 3D printing. In these scenarios, a 3D printed model of mask data that is missing portions or includes extraneous portions can result in a 3D printed model that does not fully represent the object. For medical application, a doctor can use the 3D printed model or the visualized mask data to learn about/practice operations on body part of a particular patient. If the mask data is missing portions or includes extraneous portions, the doctor may not know this is an error, and may base treatment of a patient on this erroneous data.
In industrial applications, missing mask data can result in many errors, for example, erroneous 3D printed objects. Therefore, it can be desirable to correct masks. Further it is desirable to correct masks to improve precision of the mask.
Current methods for correcting mask data typically can involve a user manually correcting the mask. For example, for 3D imaging data, a user can modify the 3D imaging data slice by slice. Current methods can require an in-depth understanding of the 3D object and/or computer image rendering by the user. Typically the person viewing the data (e.g., a doctor or an industrial process engineer) may not have a sufficient level of understanding to modify the data. Thus, manual correction can typically require two people.
These methods can also contribute to imprecision in the mask data due to, for example, human error. For example, a user may accidentally correct a portion of the 3D object that is not actually erroneous, resulting in further errors in the masks. In addition, the manual process of correcting the data can increase an amount of data used by the computer overall. For example, each slice that is modified can increase the amount of data. Thus, manual correction is impracticable.